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Multivariate nonparametric tests

delete2004-11-01
delete107
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OA
AI
H
Hannu Oja *
R
Ronald H. Randles
DOI：10.1214/088342304000000558delete
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Abstract

Abstract

En 中文
Multivariate nonparametric statistical tests of hypotheses are described for the one-sample location problem, the several-sample location problem and the problem of testing independence between pairs of vectors. These methods are based on affine-invariant spatial sign and spatial rank vectors. They provide affine-invariant multivariate generalizations of the univariate sign test, signed-rank test, Wilcoxon rank sum test, Kruskal-Wallis test, and the Kendall and Spearman correlation tests. While the emphasis is on tests of hypotheses, certain references to associated affine-equivariant estimators are included. Pitman asymptotic efficiencies demonstrate the excellent performance of these methods, particularly in heavy-tailed population settings. Moreover, these methods are easy to compute for data in common dimensions.
Keywords:
affine invariance
spatial rank
spatial sign
Pitman efficiency
robustness

Journal

Statistical Science cover
Statistical Science
IF:
3.4
Papers:
1.0K
Citations:
8.7K

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No organization information available